Privacy-preserving targeted mobile advertising: A Blockchain-based framework for mobile ads

Both of mobile multimedia and mobile Internet are the important development directions of the mobile service. However, it would take great cost by using the high data transmission rate of wireless multimedia communication service. Under the premise of not increasing the investment in hardware, the personalized service could be applied to the mobile service to not only reducing wireless multimedia communication cost but also keeping the quality of mobile service for users. This paper presents a modeling method of mobile phone user profile based on Ontology. This paper founds a model in a method of spatial graph and introduces the theory of interval valued fuzzy sets, brings forward a series of correlative definitions and formulae of founding the model and designs an Algorithm on the Spatial Graph’s Establishment and Updating. Then, it also studies the reasoning technology based on the mobile phone user profile and presents a Reasoning Algorithm on the Mobile Phone User Profile. It should be considered that we have made a useful attempt on the study of founding user profile and forecasting users’ possible requirements.

The application of location-aware technology has increasingly accumulated a large amount of user spatiotemporal data, which gives us the opportunity to analyze users' interests and behavior patterns. This paper proposes a new method for constructing user's mobility profiles and measuring their similarity. First, a new method of using the categories of points of interest (POI) to represent the semantics of a geographical area (stay region) where a user stays for a long period of time is proposed. Second, the sequential pattern mining technique is applied to extract the user's mobility profiles. Then, the user similarity is computed based on the maximum benefit assignment problem. Finally, a comparison experiment with the existing works on the real dataset verifies the effectiveness of the proposed approach.

Mobile user profiling refers to the efforts of extracting users’ characteristics from mobile activities. In order to capture the dynamic varying of user characteristics for generating effective user profiling, we propose an imitation-based mobile user profiling framework. Considering the objective of teaching an autonomous agent to imitate user mobility based on the user’s profile, the user profile is the most accurate when the agent can perfectly mimic the user behavior patterns. The profiling framework is formulated into a reinforcement learning task, where an agent is a next-visit planner, an action is a POI that a user will visit next, and the state of the environment is a fused representation of a user and spatial entities. An event in which a user visits a POI will construct a new state, which helps the agent predict users’ mobility more accurately. In the framework, we introduce a spatial Knowledge Graph (KG) to characterize the semantics of user visits over connected spatial entities. Additionally, we develop a mutual-updating strategy to quantify the state that evolves over time. Along these lines, we develop a reinforcement imitative graph learning framework for mobile user profiling. Finally, we conduct extensive experiments to demonstrate the superiority of our approach.

Mobile user profiles are a summary of characteristics of user-specific mobile activities. Mobile user profiling is to extract a user's interest and behavioral patterns from mobile behavioral data. While some efforts have been made for mobile user profiling, existing methods can be improved via representation learning with awareness of substructures in users' behavioral graphs. Specifically, in this paper, we study the problem of mobile users profiling with POI check-in data. To this end, we first construct a graph, where a vertex is a POI category and an edge is the transition frequency of a user between two POI categories, to represent each user. We then formulate mobile user profiling as a task of representation learning from user behavioral graphs. We later develop a deep adversarial substructured learning framework for the task. This framework has two mutually-enhanced components. The first component is to preserve the structure of the entire graph, which is formulated as an encoding-decoding paradigm. In particular, the structure of the entire graph is preserved by minimizing reconstruction loss between an original graph and a reconstructed graph. The second component is to preserve the structure of subgraphs, which is formulated as a substructure detector based adversarial training paradigm. In particular, this paradigm includes a substructure detector and an adversarial trainer. Instead of using non-differentiable substructure detection algorithms, we pre-train a differentiable convolutional neural network as the detector to approximate these detection algorithms. The adversarial trainer is to match the detected substructure of the reconstructed graph to the detected substructure of the original graph. Also, we provide an effective solution for the optimization problems. Moreover, we exploit the learned representations of users for the next activity type prediction. Finally, we present extensive experimental results to demonstrate the improved performances of the proposed method.

In recent years, smartphone has become the popular gadgets among many people. It gives a way for innovating new things in computation and communication technology and provided a value added service for users. At present, the digital advertisement system has chosen the smartphone apps instead of world wide web(www) because of its futures like mobility, portable and its rich content display device. But the mobile advertisement system lacks in publishing right advertisement to the right people at right location. After analyzing the various difficulties in mobile advertising which does not reach the customer efficiently, we propose an efficient and high scalable digital advertisement system on cloud for location based mobile advertising, which enable the mobile users to get the push type advertisement which are local to them or even some times the advertisement which are relevant to them based on the user profile and location coordinates of the mobile base station (MBT). By this approach the advertisement cost is low compared to the traditional advertisement made through web and mass media. The performance of the digital advertisement system in terms of search cost and throughput is done and we estimate the product demand based on advertisement ratings or click ratings made by users.

Grids enable uniform access to resources by implementing standard interfaces to resource gateways. In the Open Science Grid (OSG), privileges are granted on the basis of the user's membership to a Virtual Organization (VO). However, Grid sites are solely responsible to determine and control access privileges to resources using users' identity and personal attributes, which are available through Grid credentials. While this guarantees full control on access rights to the sites, it makes VO privileges heterogeneous throughout the Grid and hardly fits with the Grid paradigm of uniform access to resources. To address these challenges, we are developing the Scalable Virtual Organization Privileges Management Environment (SVOPME), which provides tools for VOs to define and publish desired privileges and assists sites to provide the appropriate access policies. Moreover, SVOPME provides tools for Grid sites to analyze site access policies for various resources, verify compliance with preferred VO policies, and generate directives for site administrators on how the local access policies can be amended to achieve such compliance without taking control of local configurations away from site administrators. This paper discusses what access policies are of interest to the OSG community and how SVOPME implements privilege management for OSG.

The unprecedented growth in mobile device adoption and the rapid advancement of mobile technologies a wireless networks have created new opportunities in mobile marketing and adverting. The opportunities for Mobile Marketers and Advertisers include real-time customer engagement, improve customer experience, build brand loyalty, increase revenues, and drive customer satisfaction. The challenges, however, for the Marketers and Advertisers include how to analyze troves of data that mobile devices emit and how to derive customer engagement insights from the mobile data. This research paper addresses the challenge by developing Big Data Mobile Marketing analytics and advertising recommendation framework. The proposed framework supports both offline and online advertising operations in which the selected analytics techniques are used to provide advertising recommendations based on collected Big Data on mobile user's profiles, access behaviors, and mobility patterns. The paper presents prototyping solution design as well as its application and certain experimental results.

Adaptive beamforming and user association in heterogeneous cloud radio access networks: A mobility-aware performance-cost trade-off

Social network is a critical component in mobile multimedia systems, where users share their videos, photos, and other media. However, the information (e.g., posts, user profiles, etc.) shared on the social network platforms usually reflects many users’ personal (private) information, which could be mined and abused for malicious purposes. To address privacy concerns, many social network service providers adopted privacy-preserving mechanisms, e.g., anonymizing user identity, hiding users’ profiles, etc. As a result, the attributes in user profiles are usually set up to be accessed only by friends to prevent privacy leakage. Several attacks have been proposed to infer the hidden attributes to Several the efficiency of current privacy-protecting mechanisms. Most of these solutions are based on the social links among users or their behaviors. In this paper, we systematically analyze the social features related to user privacy inference and found that there are relevances among social attributes, which has a great impact on inferring users’ hidden attributes. According to our findings, we propose an efficient social attribute inference scheme based on social links and attribute relevance properties. We develop a relevance attribute inference method (ReAI) using random walks with restart. We analyze attribute relevance on inference performance and use Kulczynski measure to quantify attribute relevance as edge weights of attribute nodes in an improved social-attribute network. We evaluate our method and compare it with the traditional attribute inference method. The results show that our method performs better than the traditional method. We also use Kulczynski measure and Information Gain Ratio to evaluate the improvements. The results show that the bigger relevance between attributes contributes to higher improvements.

Mobile device can be used as a medium to send and receive the mobile internet content. However, there are several limitations using mobile internet. Content personalisation has been viewed as an important area when using mobile internet. In order for personalisation to be successful, understanding the user is important. In this paper, we explore the implementation of the user profile at client-side, which may be used whenever user connect to the mobile content provider. The client-side user profile can help to free the provider in performing analysis by using data mining technique at the mobile device. This research investigates the conceptual idea of using clustering and classification of user profile at the client-site mobile. In this paper, we applied K-means and compared several other classification algorithms like TwoStep, Kohenen and Anomaly to determine the boundaries of the important factors using information ranking separation.

The fast expansion during the recent years of online social networks, such as Twitter, Facebook, or Foursquare, is making available an enormous and continuous stream of user-generated contents including information on human mobility within urban context. In particular, online social networks allows for the collection of geo-tagged data obtained through the GPS readings of phones through which users have the possibility to tag posts, photos and videos with geographical coordinates. In this context, recommending the future position of a mobile object is key for the implementations of several applications aiming at improving mobility within urban areas. The paper proposes a location recommendation approach that exploits geo-tagged data on social networks. The approach integrates user preference, sequential mobility and geographic constraints. The recommendation task is formulated as a similarity problem among the visiting and mobility profiles of users, accounting the mobility sequentiality in the patterns. Two ranking metrics are introduced to predict places the user could like. The metrics are then combined into an overall recommendation ranking function. The candidate locations are then ranked according to the two similarity measures. The experimental results obtained by using a real-world dataset of tweets show that the proposed method is effective in recommending unseen locations, outperforming representative state-of-the-art approaches.

User modeling for inferring user interests from Online Social Networks (OSNs) such as Twitter has received great attention in the user modeling community with the growing popularity of OSNs. The focus of previous works has been on analyzing user-generated content such as tweets to infer user interests. Therefore, these previous studies were limited to active users who have been actively generating content. On the other hand, with the percentage of passive use of OSNs on the rise, some researchers investigated different types of information about followees (i.e., people that a user is following) such as tweets, usernames, and biographies to infer user interests for passive users who use OSNs for consuming information from followees but who do not produce any content. Although different types of information about followees have been exploited, list memberships (a topical list which other Twitter users can freely add a user into) of followees have not yet been investigated extensively for inferring user interests. In this paper, we investigate list memberships of followees, to infer interest profiles for passive users. To this end, we propose user modeling strategies with two different weighting schemes as well as a refined interest propagation strategy based on previous work. In addition, we investigate whether the information from biographies and list memberships of followees can complement each other, and thus improve the quality of inferred interest profiles for passive users. Results show that leveraging list memberships of followees is useful for inferring user interests when the number of followees is relatively small compared to using biographies of followees. In addition, we found that combining the two different types of information (list memberships and biographies) of followees can improve the quality of user interest profiles significantly compared to a state-of-art method in the context of link recommendations on Twitter.

The problem of activity recognition is a topic that has been explored in the field of ubiquitous computing, the popularization of sensors on the most diverse types has been instrumental in improving the effectiveness of recognition. Smartphones offer a range of sensors (GPS, Accelerometer, Gyroscopes, etc.) that can be used to provide data for this type of problem. This work proposes a new model of activity recognition in GPS captured data and enriched with POIs (Points Of Interest) and user profile. The experiment was performed by 10 volunteers collecting data for 10 days. The model aims to recognize 13 different activities, divided into stop activities (Bank, breakfast, dining, lunch, praying, recreation, shopping, studying, waiting transport and working) and moves activities (in a car, on a bus and walking). The model was tested and compared to J48, SVM, ANN and RF algorithms, and obtained 97.4% hits.

Recommender systems are software applications that provide or suggest items to intended users. These systems use filtering techniques to provide recommendations. The major ones of these techniques are collaborative-based filtering technique, content-based technique, and hybrid algorithm. The motivation came as a result of the need to integrate recommendation feature in digital libraries in order to reduce information overload. Content-based technique is adopted because of its suitability in domains or situations where items are more than the users. TF-IDF (Term Frequency Inverse Document Frequency) and cosine similarity were used to determine how relevant or similar a research paper is to a user's query or profile of interest. Research papers and user's query were represented as vectors of weights using Keyword-based Vector Space model. The weights indicate the degree of association between a research paper and a user's query. This paper also presents an algorithm to provide or suggest recommendations based on users' query. The algorithm employs both TF-IDF weighing scheme and cosine similarity measure. Based on the result or output of the system, integrating recommendation feature in digital libraries will help library users to find most relevant research papers to their needs.

In a smart advertising network ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SAN</i> ), a broker builds user profiles from its wealth of user data, manages advertisements for retailers, and disseminates the advertisements through multiple channels. However, the broker sometimes provides insufficient transparency explanations of advertising activities, which may result in the increasing popularity of ad-blocking software and lower advertising investments from retailers. In this paper, we propose a blockchain-based Smart Advertising Network with Privacy-preserving Accountability ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SANPA</i> ). Specifically, we design a composite Succinct Non-interactive Argument ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SNARG</i> ) system, that commits advertising policies as cryptographic authenticators in a smart contract. By doing so, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SANPA</i> is compatible with the existing <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SAN</i> without posing prohibitive implementation cost over the blockchain architecture. Users or retailers can require explanations of an advertising activity by sending a challenge to the smart contract. With the succinctness and privacy preservation of the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SNARG</i> system, the smart contract can efficiently verify whether the challenged advertising activity follows committed advertising policies without exposing user profile privacy. If any misconduct is identified, the contract enforces public accountability on the misbehaving party by confiscating its cryptocurrency deposits. We conduct extensive experiments to provide both on-chain and off-chain benchmarks, which demonstrates the application feasibility of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SANPA</i> .